Memory activation enhances EEG abnormality in mild cognitive impairment

The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative Disorder Detection

Neurocognitive Disorders (NCDs) pose a significant global health concern, and early detection is crucial for optimizing therapeutic outcomes. In parallel, mobile health apps (mHealth apps) have emerged as a promising avenue for assisting individuals with cognitive deficits. Under this perspective, we pioneered the development of the RODI mHealth app, a unique method for detecting aligned with the criteria for NCDs using a series of brief tasks. Utilizing the RODI app, we conducted a study from July to October 2022 involving 182 individuals with NCDs and healthy participants. The study aimed to assess performance differences between healthy older adults and NCD patients, identify significant performance disparities during the initial administration of the RODI app, and determine critical features for outcome prediction. Subsequently, the results underwent machine learning processes to unveil underlying patterns associated with NCDs. We prioritize the tasks within RODI based on their alignment with the criteria for NCDs, thus acting as key digital indicators for the disorder. We achieve this by employing an ensemble strategy that leverages the feature importance mechanism from three contemporary classification algorithms. Our analysis revealed that tasks related to visual working memory were the most significant in distinguishing between healthy individuals and those with an NCD. On the other hand, processes involving mental calculations, executive working memory, and recall were less influential in the detection process. Our study serves as a blueprint for future mHealth apps, offering a guide for enhancing the detection of digital indicators for disorders and related conditions.

Resting state electroencephalographic rhythms are affected by immediately preceding memory demands in cognitively unimpaired elderly and patients with mild cognitive impairment

Experiments on event-related electroencephalographic oscillations in aged people typically include blocks of cognitive tasks with a few minutes of interval between them. The present exploratory study tested the effect of being engaged on cognitive tasks over the resting state cortical arousal after task completion, and whether it differs according to the level of the participant’s cognitive decline. To investigate this issue, we used a local database including data in 30 healthy cognitively unimpaired (CU) persons and 40 matched patients with amnestic mild cognitive impairment (aMCI). They had been involved in 2 memory tasks for about 40 min and underwent resting-state electroencephalographic (rsEEG) recording after 5 min from the task end. eLORETA freeware estimated rsEEG alpha source activity as an index of general cortical arousal. In the CU but not aMCI group, there was a negative correlation between memory tasks performance and posterior rsEEG alpha source activity. The better the memory tasks performance, the lower the posterior alpha activity (i.e., higher cortical arousal). There was also a negative correlation between neuropsychological test scores of global cognitive status and alpha source activity. These results suggest that engagement in memory tasks may perturb background brain arousal for more than 5 min after the tasks end, and that this effect are dependent on participants global cognitive status. Future studies in CU and aMCI groups may cross-validate and extend these results with experiments including (1) rsEEG recordings before memory tasks and (2) post-tasks rsEEG recordings after 5, 15, and 30 min.

Synergy through Integration of Wearable EEG and Virtual Reality for Mild Cognitive Impairment and Mild Dementia Screening

Virtual reality (VR) technologies have shown promising potential in the early diagnosis of dementia by enabling accessible and regular assessment. However, previous VR studies were restricted to the analysis of behavioral responses, so information about degenerated brain dynamics could not be directly acquired. To address this issue, we provide a cognitive impairment (CI) screening tool based on a wearable EEG device integrated into a VR platform. Subjects were asked to use a hardware setup consisting of a frontal six-channel EEG device mounted on a VR device and to perform four cognitive tasks in VR. Behavioral response profiles and EEG features were extracted during the tasks, and classifiers were trained on extracted features to differentiate subjects with CI from healthy controls (HCs). Notably, the performance of the patient classification consistently improved when EEG characteristics measured during cognitive tasks were additionally included in feature attributes than when only the task scores or resting-state EEG features were used, suggesting that our protocol provides discriminative information for screening. These results propose that the integration of EEG devices into a VR framework could emerge as a powerful and synergistic strategy for constructing an easily accessible EEG-based dementia screening tool.

EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance

Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints.

Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network.

Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution.

Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.

Quantitative EEG and cholinergic basal forebrain atrophy in Parkinson's disease and mild cognitive impairment

Cholinergic degeneration is a key feature of dementia in neurodegenerative conditions including Alzheimer's disease (AD) and Parkinson's disease (PD). Quantitative electro-encephalography (EEG) metrics are altered in both conditions from early stages, and recent research in people with Lewy body and AD dementia suggests these changes may be associated with atrophy in cholinergic basal forebrain nuclei (cBF). To determine if these relationships exist in predementia stages of neurodegenerative conditions, we studied resting-state EEG and in vivo cBF volumes in 31 people with PD (without dementia), 21 people with mild cognitive impairment (MCI), and 21 age-matched controls. People with PD showed increased power in slower frequencies and reduced alpha reactivity compared to controls. Volumes of cholinergic cell clusters corresponding to the medial septum and vertical and horizontal limb of the diagonal band, and the posterior nucleus basalis of Meynert, correlated positively with; alpha reactivity in people with PD (p< 0.01); and pre-alpha power in people with MCI (p< 0.05). These results suggest that alpha reactivity and pre-alpha power are related to changes in cBF volumes in MCI and PD without dementia.

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Background:

Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated.

Objective:

To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline.

Methods:

We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available.

Results:

Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures.

Conclusion:

Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.

Spatiotemporal Oscillatory Patterns During Working Memory Maintenance in Mild Cognitive Impairment and Subjective Cognitive Decline

Working memory (WM) is a crucial cognitive process and its disruption is among the earliest symptoms of Alzheimer’s disease. While alterations of the neuronal processes underlying WM have been evidenced in mild cognitive impairment (MCI), scarce literature is available in subjective cognitive decline (SCD). We used magnetoencephalography during a WM task performed by MCI [Formula: see text], SCD [Formula: see text] and healthy elders [Formula: see text] to examine group differences during the maintenance period (0–4000[Formula: see text]ms). Data were analyzed using time–frequency analysis and significant oscillatory differences were localized at the source level. Our results indicated significant differences between groups, mainly during the early maintenance (250–1250[Formula: see text]ms) in the theta, alpha and beta bands and in the late maintenance (2750–3750[Formula: see text]ms) in the theta band. MCI showed lower local synchronization in fronto-temporal cortical regions in the early theta–alpha window relative to controls [Formula: see text] and SCD [Formula: see text], and in the late theta window relative to controls [Formula: see text] and SCD [Formula: see text]. Early theta–alpha power was significantly correlated with memory scores [Formula: see text] and late theta power was correlated with task performance [Formula: see text] and functional activity scores [Formula: see text]. In the early beta window, MCI showed reduced power in temporo-posterior regions relative to controls [Formula: see text] and SCD [Formula: see text]. Our results may suggest that these alterations would reflect that memory-related networks are damaged.

Changes in Theta but not Alpha Modulation Are Associated with Impairment in Working Memory in Alzheimer's Disease and Mild Cognitive Impairment

While several studies have found that neural oscillations play a key role in the functioning of working memory, the nature of aberrant oscillatory activity underlying working memory impairments in Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains largely unexplored. These individuals often display structural alterations in brain regions and pathways involved in working memory processes and therefore may also display altered oscillatory activity during memory activation. Electroencephalographic (EEG) activity was recorded during the N-back working memory task in three groups: AD (n = 29), MCI (n = 100), and healthy controls (HCs; n = 40). Theta (4-7 Hz) and alpha (7.5-12 Hz) modulation was measured in response to the stimulus presentation during correct and incorrect responses. This modulation represents the change in EEG activity associated with the stimulus onset and was measured as a ratio of post stimulus power to pre stimulus power. We also assessed the relationship between change in oscillatory power and working memory performance. Compared to HCs, the AD group demonstrated the lowest working memory accuracy and a smaller theta ratio for correct responses on the 2-back condition; the MCI group demonstrated a smaller theta ratio for correct responses on the 3-back condition. Finally, we observed that the theta ratio, but not the alpha ratio, was a significant predictor of working memory performance in the three groups for all conditions. Taken together, these behavioral and electrophysiological results suggest that in addition to impairments in working memory performance, modulation of theta, but not alpha power, may be impaired in MCI and AD.

Deficit in specific cognitive domains associated with dementia in Parkinson's disease

Impairment in different cognitive domains such as executive functions, language, memory and visuospatial skills occur frequently in Parkinson disease (PD) leading to significant disability and deterioration in quality of life. Heterogeneity of cognitive impairment enhances risk of developing dementia as disease progress. The objective is to explore the pattern of cognitive impairment with reference to the affected domains in PD with or without dementia relative to healthy controls. In this study, 110 PD patients and 26 healthy control were categorized into groups using Mini Mental State Examination and Clinical Dementia Rating scores as PD without dementia (PDND, n = 65; MMSE score >24; CDR = 0-1), PD with dementia (PDD, n = 45; MMSE score ≤24; CDR = 0.5-3) and healthy control (HC, n = 26; MMSE score >26; CDR = 0). Both Patients and controls underwent individual assessments of working memory, semantic memory, attention, language, executive functions, psychomotor and visuospatial skills and dementia using different cognitive function tests. Findings revealed lower scores of word memory, attention, psychomotor speed, visuospatial skills and executive functions in PDD compared to PDND. Interestingly, in PDD scores of picture memory, semantic memory and language functions were comparable with PDND. Compared to HC, PDND had no impairment in working memory, attention and executive functions, whereas PDD had lower scores in all the cognitive domains tested. Results indicate that the deficits in word memory, attention, psychomotor speed, visuospatial skills and executive functions distinguishes PDD from PDND. Impairment in specific cognitive domains may be a biomarker for predicting onset of dementia in Parkinson's disease.

Dissociating Normal Aging from Alzheimer's Disease: A View from Cognitive Neuroscience

Both normal aging and Alzheimer's disease (AD) are associated with changes in cognition, grey and white matter volume, white matter integrity, neural activation, functional connectivity, and neurotransmission. Obviously, all of these changes are more pronounced in AD and proceed faster providing the basis for an AD diagnosis. Since these differences are quantitative, however, it was hypothesized that AD might simply reflect an accelerated aging process. The present article highlights the different neurocognitive changes associated with normal aging and AD and shows that, next to quantitative differences, there are multiple qualitative differences as well. These differences comprise different neurocognitive dissociations as different cognitive deficit profiles, different weights of grey and white matter atrophy, and different gradients of structural decline. These qualitative differences clearly indicate that AD cannot be simply described as accelerated aging process but on the contrary represents a solid entity.

Attention and Working Memory-Related EEG Markers of Subtle Cognitive Deterioration in Healthy Elderly Individuals

Future treatments of Alzheimer's disease need the identification of cases at high risk at the preclinical stage of the disease before the development of irreversible structural damage. We investigated here whether subtle cognitive deterioration in a population of healthy elderly individuals could be predicted by EEG signals at baseline under cognitive activation. Continuous EEG was recorded in 97 elderly control subjects and 45 age-matched mild cognitive impairment (MCI) cases during a simple attentional and a 2-back working memory task. Upon 18-month neuropsychological follow-up, the final sample included 55 stable (sCON) and 42 deteriorated (dCON) controls. We examined the P1, N1, P3, and PNwm event-related components as well as the oscillatory activities in the theta (4-7 Hz), alpha (8-13 Hz), and beta (14-25 Hz) frequency ranges (ERD/ERS: event-related desynchronization/synchronization, and ITC: inter-trial coherence). Behavioral performance, P1, and N1 components were comparable in all groups. The P3, PNwm, and all oscillatory activity indices were altered in MCI cases compared to controls. Only three EEG indices distinguished the two control groups: alpha and beta ERD (dCON >  sCON) and beta ITC (dCON <  sCON). These findings show that subtle cognitive deterioration has no impact on EEG indices associated with perception, discrimination, and working memory processes but mostly affects attention, resulting in an enhanced recruitment of attentional resources. In addition, cognitive decline alters neural firing synchronization at high frequencies (14-25 Hz) at early stages, and possibly affects lower frequencies (4-13 Hz) only at more severe stages.

Neural correlates of true and false memory in mild cognitive impairment

The goal of this research was to investigate the changes in neural processing in mild cognitive impairment. We measured phase synchrony, amplitudes, and event-related potentials in veridical and false memory to determine whether these differed in participants with mild cognitive impairment compared with typical, age-matched controls. Empirical mode decomposition phase locking analysis was used to assess synchrony, which is the first time this analysis technique has been applied in a complex cognitive task such as memory processing. The technique allowed assessment of changes in frontal and parietal cortex connectivity over time during a memory task, without a priori selection of frequency ranges, which has been shown previously to influence synchrony detection. Phase synchrony differed significantly in its timing and degree between participant groups in the theta and alpha frequency ranges. Timing differences suggested greater dependence on gist memory in the presence of mild cognitive impairment. The group with mild cognitive impairment had significantly more frontal theta phase locking than the controls in the absence of a significant behavioural difference in the task, providing new evidence for compensatory processing in the former group. Both groups showed greater frontal phase locking during false than true memory, suggesting increased searching when no actual memory trace was found. Significant inter-group differences in frontal alpha phase locking provided support for a role for lower and upper alpha oscillations in memory processing. Finally, fronto-parietal interaction was significantly reduced in the group with mild cognitive impairment, supporting the notion that mild cognitive impairment could represent an early stage in Alzheimer's disease, which has been described as a 'disconnection syndrome'.

Improving the specificity of EEG for diagnosing Alzheimer's disease

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α₁ (7.5–9.5 Hz), α₂ (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD.

Electromyographic activity in the EEG in Alzheimer's disease: noise or signal?

Many efforts have been directed at negating the influence of electromyographic (EMG) activity on the EEG, especially in elderly demented patients. We wondered whether these “artifacts” might reflect cognitive and behavioural aspects of dementia. In this pilot study, 11 patients with probable Alzheimer's disease (AD), 13 with amnestic mild cognitive impairment (MCI) and 13 controls underwent EEG registration. As EMG measures, we used frontal and temporal 50–70 Hz activity. We found that the EEGs of AD patients displayed more theta activity, less alpha reactivity, and more frontal EMG than controls. Interestingly, increased EMG activity indicated more cognitive impairment and more depressive complaints. EEG variables on the whole distinguished better between groups than EMG variables, but an EMG variable was best for the distinction between MCI and controls. Our results suggest that EMG activity in the EEG could be more than noise; it differs systematically between groups and may reflect different cerebral functions than the EEG.

Delays in neural processing during working memory encoding in normal aging

Declines in neural processing speed have been proposed to underlie a broad range of cognitive deficits in older adults. However, the impact of delays in neural processing during stimulus encoding on working memory (WM) performance is not well understood. In the current study, we assessed the influence of aging on the relationship between neural measures of processing speed and WM performance during a selective delayed-recognition task for color and motion stimuli, while electroencephalography (EEG) was recorded in young and older adults. A latency delay was observed for the selection negativity (SN) and alpha band activity (measures of attentional allocation) in older adults during WM encoding of both motion and color stimuli, with the latency and magnitude of the SN predicting subsequent recognition performance. Furthermore, an age-related delay in the N1 latency occurred specifically during the encoding of color stimuli. These results suggest that the presence of both generalized feature-based and feature-specific deficits in the speed of selective encoding of information contributes to WM performance deficits in older adults.

EEG Patterns in Mild Cognitive Impairment (MCI) Patients

An emerging clinical priority for the treatment of Alzheimer's disease (AD) is the implementation of therapies at the earliest stages of disease onset. All AD patients pass through an intermediary stage of the disorder known as Mild Cognitive Impairment (MCI), but not all patients with MCI develop AD. By applying computer based signal processing and pattern recognition techniques to the electroencephalogram (EEG), we were able to classify AD patients versus controls with an accuracy rate of greater than 80%. We were also able to categorize MCI patients into two subgroups: those with EEG Beta power profiles resembling AD patients and those more like controls. We then used this brain-based classification to make predictions regarding those MCI patients most likely to progress to AD versus those who would not. Our classification algorithm correctly predicted the clinical status of 4 out of 6 MCI patients returning for 2 year clinical follow-up. While preliminary in nature, our results suggest that automated pattern recognition techniques applied to the EEG may be a useful clinical tool not only for classification of AD patients versus controls, but also for identifying those MCI patients most likely to progress to AD.

EEG Patterns in Mild Cognitive Impairment (MCI) Patients

An emerging clinical priority for the treatment of Alzheimer’s disease (AD) is the implementation of therapies at the earliest stages of disease onset. All AD patients pass through an intermediary stage of the disorder known as Mild Cognitive Impairment (MCI), but not all patients with MCI develop AD. By applying computer based signal processing and pattern recognition techniques to the electroencephalogram (EEG), we were able to classify AD patients versus controls with an accuracy rate of greater than 80%. We were also able to categorize MCI patients into two subgroups: those with EEG Beta power profiles resembling AD patients and those more like controls. We then used this brain-based classification to make predictions regarding those MCI patients most likely to progress to AD versus those who would not. Our classification algorithm correctly predicted the clinical status of 4 out of 6 MCI patients returning for 2 year clinical follow-up. While preliminary in nature, our results suggest that automated pattern recognition techniques applied to the EEG may be a useful clinical tool not only for classification of AD patients versus controls, but also for identifying those MCI patients most likely to progress to AD.

Split-test Bonferroni correction for QEEG statistical maps

With statistical testing, corrections for multiple comparisons, such as Bonferroni adjustments, have given rise to controversies in the scientific community, because of their negative impact on statistical power. This impact is especially problematic for high-multidimensional data, such as multi-electrode brain recordings. With brain imaging data, a reliable method is needed to assess statistical significance of the data without losing statistical power. Conjunction analysis allows the combination of significance and consistency of an effect. Through a balanced combination of information from retest experiments (multiple trials split testing), we present an intuitively appealing, novel approach for brain imaging conjunction. The method is then tested and validated on synthetic data followed by a real-world test on QEEG data from patients with Alzheimer's disease. This latter application requires both reliable type-I error and type-II error rates, because of the poor signal-to-noise ratio inherent in EEG signals.

Abnormal-induced theta activity supports early directed-attention network deficits in progressive MCI

The electroencephalography (EEG) theta frequency band reacts to memory and selective attention paradigms. Global theta oscillatory activity includes a posterior phase-locked component related to stimulus processing and a frontal-induced component modulated by directed attention. To investigate the presence of early deficits in the directed attention-related network in elderly individuals with mild cognitive impairment (MCI), time-frequency analysis at baseline was used to assess global and induced theta oscillatory activity (4-6Hz) during n-back working memory tasks in 29 individuals with MCI and 24 elderly controls (EC). At 1-year follow-up, 13 MCI patients were still stable and 16 had progressed. Baseline task performance was similar in stable and progressive MCI cases. Induced theta activity at baseline was significantly reduced in progressive MCI as compared to EC and stable MCI in all n-back tasks, which were similar in terms of directed attention requirements. While performance is maintained, the decrease of induced theta activity suggests early deficits in the directed-attention network in progressive MCI, whereas this network is functionally preserved in stable MCI.

EEG correlates in the spectrum of cognitive decline

OBJECTIVE

To investigate relations between EEG measures and performance on tests of global cognition, memory, language and executive functioning.

METHODS

Twenty-two controls, 18 patients with mild cognitive impairment (MCI) and 16 with probable Alzheimer's disease (AD) underwent neuropsychological and EEG investigations. We used the following EEG measures: theta relative power during eyes closed, alpha reactivity during memory activation (i.e. the percentual decrease in alpha power as compared to eyes closed) and alpha coherence during eyes closed and memory activation.

RESULTS

Theta relative power was increased in AD patients as compared with controls (p<0.001) and MCI patients (p<0.01) and related to decreased performance in all cognitive domains. Alpha reactivity was decreased in AD patients as compared with controls (p<0.005) and related to decreased performance on tests of global cognition, memory and executive functioning. Alpha coherence did not differ between groups and was unrelated to cognition.

CONCLUSIONS

EEG power measures were associated with decreased performance on tests of global cognition, memory, language and executive functioning, while coherence measures were not.

SIGNIFICANCE

The EEG yielded several power measures related to cognitive functions. These EEG power measures might prove useful in prospective studies aimed at predicting longitudinal cognitive decline and dementia.

EEG and MRI correlates of mild cognitive impairment and Alzheimer's disease

OBJECTIVE

To investigate whether cognitive function in the spectrum of normal aging to Alzheimer's disease is better reflected in MRI or EEG measures, or a combination of both.

METHODS

Cognitive functions were tested in 33 elderly subjects: 10 with probable Alzheimer's disease, 11 with mild cognitive impairment and 12 controls. Structural brain parameters were derived from conventional MRI and a quantitative MR technique called magnetization transfer imaging. The EEG provided measures of brain function. We performed multiple linear regression analyses to relate EEG and MRI parameters to global cognition, memory, language and psychomotor speed.

RESULTS

The model showed EEG alpha reactivity during eyes open to be the primary factor associated with global cognition, memory and language skills. Brain atrophy was the primary factor associated with psychomotor speed. Furthermore, EEG alpha reactivity during eyes open explained significant additional variability in psychomotor speed.

CONCLUSION

EEG and MRI are each associated with different aspects of cognitive function and complement each other in their relations to psychomotor speed.

Changes of EEG spectra and coherence following performance in a cognitive task in Alzheimer's disease

Electroencephalographic measures combined with cognitive tasks are widely used for the assessment of cognitive and pathophysiological changes in Alzheimer's disease (AD). Instead of the analysis of EEG data obtained during the performance of the task, in this study data recorded in the immediate after-task period were analyzed. It was expected that this period would correspond to the electrophysiological consequences of the cognitive effort. Data of 14 patients with AD (MMS score: 16-24) were compared to that of 10 healthy control subjects. Reverse counting of a fix duration was used as a cognitive task. Changes of relative frequency spectra, and those of inter-and intrahemispheric coherence were analyzed. Relative theta power was significantly higher in AD patients compared to the controls both before and after the task. The performance of the task resulted in an increase of the relative alpha2 band in the AD group, whereas it slightly decreased in the control group. The most prominent coherence differences between AD and controls were found in the alpha1 band, especially for long-range coherence values. Coherence in this frequency band increased in the control group following the task, not seen in the AD group. We conclude that EEG parameters calculated from epochs following the completion of a cognitive task clearly differentiates patients with AD from normal controls. The electrophysiological changes found in AD may correspond to the decrease of functional connectivity of cortical areas and to the malfunctioning of the networks engaged in the cognitive task investigated.

Clinical neurophysiology of aging brain: from normal aging to neurodegeneration

Physiological brain aging is characterized by a loss of synaptic contacts and neuronal apoptosis that provokes age-dependent decline of sensory processing, motor performance, and cognitive function. Neural redundancy and plastic remodelling of brain networking, also secondary to mental and physical training, promotes maintenance of brain activity in healthy elderly for everyday life and fully productive affective and intellectual capabilities. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Oscillatory electromagnetic brain activity is a hallmark of neuronal network function in various brain regions. Modern neurophysiological techniques including electroencephalography (EEG), event-related potential (ERP), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) can accurately index normal and abnormal brain aging to facilitate non-invasive analysis of cortico-cortical connectivity and neuronal synchronization of firing and coherence of rhythmic oscillations at various frequencies. The present review provides a perspective of these issues by assaying different neurophysiological methods and integrating the results with functional brain imaging findings. It is concluded that discrimination between physiological and pathological brain aging clearly emerges at the group level, with applications at the individual level also suggested. Integrated approaches utilizing neurophysiological techniques together with biological markers and structural and functional imaging are promising for large-scale, low-cost and non-invasive evaluation of at-risk populations. Practical implications of the methods are emphasized.

Memory activation reveals abnormal EEG in preclinical Huntington's disease

The EEG is potentially useful as a marker of early Huntington's disease (HD). In dementia, the EEG during a memory activation challenge showed abnormalities where the resting EEG did not. We investigated whether memory activation also reveals EEG abnormalities in preclinical HD. Sixteen mutation carriers for HD and 13 nonmutation carriers underwent neurological, neuropsychological, MRI and EEG investigations. The EEG was registered during a rest condition, i.e. eyes closed, and a working memory task. In each condition we determined absolute power in the theta (4-8 Hz) and alpha (8-13 Hz) bands and subsequently calculated relative alpha power. The EEG during eyes closed did not differ between groups. The EEG during memory activation showed less relative alpha power in mutation carriers as compared to nonmutation carriers, even though memory performance was similar [F (1,27) = 10.87; P = 0.003]. Absolute powers also showed less alpha power [F (1,27) = 7.02; P = 0.013] but similar theta power. No correlations were found between absolute and relative alpha power on the one hand and neuropsychological scores, motor scores or number of CAG repeats on the other. In conclusion, memory activation reveals functional brain changes in Huntington's disease before clinical signs become overt.